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Keynote talk
Workshop: Optimal Transport and Machine Learning

Variational inference via Wasserstein gradient flows (Sinho Chewi)

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Sat 16 Dec 2:30 p.m. PST — 3 p.m. PST


Probabilistic problems which involve the non-smooth entropy functional benefit from the design of proximal I will showcase the use of Wasserstein gradient flows as a conceptual framework for developing principled algorithms for variational inference (VI) with accompanying convergence guarantees, particularly for Gaussian VI and mean-field VI. This is joint work with Francis Bach, Krishnakumar Balasubramanian, Silvère Bonnabel, Michael Diao, Yiheng Jiang, Marc Lambert, Aram-Alexandre Pooladian, Philippe Rigollet, and Adil Salim.

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